A Two-Step Model-Based Reconstruction and Imaging Method for Baseline-Free Lamb Wave Inspection
Traditional Lamb wave inspection and imaging methods heavily rely on prior knowledge of dispersion curves and baseline recordings, which may not be feasible in the majority of real cases due to production uncertainties and environmental variations. In order to solve this problem, a two-step Lamb wav...
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MDPI AG
2023-05-01
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Series: | Symmetry |
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Online Access: | https://www.mdpi.com/2073-8994/15/6/1171 |
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author | Hang Fan Fei Gao Wenhao Li Kun Zhang |
author_facet | Hang Fan Fei Gao Wenhao Li Kun Zhang |
author_sort | Hang Fan |
collection | DOAJ |
description | Traditional Lamb wave inspection and imaging methods heavily rely on prior knowledge of dispersion curves and baseline recordings, which may not be feasible in the majority of real cases due to production uncertainties and environmental variations. In order to solve this problem, a two-step Lamb wave strategy utilizing adaptive multiple signal classification (MUSIC) and sparse reconstruction of dispersion reconstruction is proposed. The multimodal Lamb waves are initially reconstructed in the <i>f-k</i> domain using random measurements, allowing for the identification and characterization of multimodal Lamb waves. Then, using local polynomial expansion and derivation, the phase and group velocities for each Lamb wave mode could be computed. Thus, the steering vectors of all potential scattering Lamb waves for each grid in the scanning area can be established, thereby allowing for the formulation of the MUSIC algorithm. To increase the precision and adaptability of the MUSIC method, the local wave components resulting from potential scatters are extracted with an adaptive window, which is governed by the group velocities and distances of Lamb wave propagation. As a result, the reconstructed dispersion relations and windowed wave components can be used to highlight the scattering features. For the method investigation, both a simulation and experiment are carried out, and both the dispersion curves and damage locations can be detected. The results demonstrate that damage localization is possible without theoretical dispersion data and baseline recordings while exhibiting a considerable accuracy and resolution. |
first_indexed | 2024-03-11T01:53:33Z |
format | Article |
id | doaj.art-e0f0327edf5247009a5bd8085bc92b98 |
institution | Directory Open Access Journal |
issn | 2073-8994 |
language | English |
last_indexed | 2024-03-11T01:53:33Z |
publishDate | 2023-05-01 |
publisher | MDPI AG |
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series | Symmetry |
spelling | doaj.art-e0f0327edf5247009a5bd8085bc92b982023-11-18T12:50:27ZengMDPI AGSymmetry2073-89942023-05-01156117110.3390/sym15061171A Two-Step Model-Based Reconstruction and Imaging Method for Baseline-Free Lamb Wave InspectionHang Fan0Fei Gao1Wenhao Li2Kun Zhang3Science and Technology on Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu 610213, ChinaSchool of Reliability and Systems Engineering, Beihang University, Xueyuan Road No. 37, Haidian District, Beijing 100191, ChinaAdvanced Manufacturing Center, Ningbo Institute of Technology, Beihang University, Ningbo 315100, ChinaScience and Technology on Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu 610213, ChinaTraditional Lamb wave inspection and imaging methods heavily rely on prior knowledge of dispersion curves and baseline recordings, which may not be feasible in the majority of real cases due to production uncertainties and environmental variations. In order to solve this problem, a two-step Lamb wave strategy utilizing adaptive multiple signal classification (MUSIC) and sparse reconstruction of dispersion reconstruction is proposed. The multimodal Lamb waves are initially reconstructed in the <i>f-k</i> domain using random measurements, allowing for the identification and characterization of multimodal Lamb waves. Then, using local polynomial expansion and derivation, the phase and group velocities for each Lamb wave mode could be computed. Thus, the steering vectors of all potential scattering Lamb waves for each grid in the scanning area can be established, thereby allowing for the formulation of the MUSIC algorithm. To increase the precision and adaptability of the MUSIC method, the local wave components resulting from potential scatters are extracted with an adaptive window, which is governed by the group velocities and distances of Lamb wave propagation. As a result, the reconstructed dispersion relations and windowed wave components can be used to highlight the scattering features. For the method investigation, both a simulation and experiment are carried out, and both the dispersion curves and damage locations can be detected. The results demonstrate that damage localization is possible without theoretical dispersion data and baseline recordings while exhibiting a considerable accuracy and resolution.https://www.mdpi.com/2073-8994/15/6/1171nondestructive evaluationlamb wavessparse reconstructionadaptive multiple signal classificationdamage imaging |
spellingShingle | Hang Fan Fei Gao Wenhao Li Kun Zhang A Two-Step Model-Based Reconstruction and Imaging Method for Baseline-Free Lamb Wave Inspection Symmetry nondestructive evaluation lamb waves sparse reconstruction adaptive multiple signal classification damage imaging |
title | A Two-Step Model-Based Reconstruction and Imaging Method for Baseline-Free Lamb Wave Inspection |
title_full | A Two-Step Model-Based Reconstruction and Imaging Method for Baseline-Free Lamb Wave Inspection |
title_fullStr | A Two-Step Model-Based Reconstruction and Imaging Method for Baseline-Free Lamb Wave Inspection |
title_full_unstemmed | A Two-Step Model-Based Reconstruction and Imaging Method for Baseline-Free Lamb Wave Inspection |
title_short | A Two-Step Model-Based Reconstruction and Imaging Method for Baseline-Free Lamb Wave Inspection |
title_sort | two step model based reconstruction and imaging method for baseline free lamb wave inspection |
topic | nondestructive evaluation lamb waves sparse reconstruction adaptive multiple signal classification damage imaging |
url | https://www.mdpi.com/2073-8994/15/6/1171 |
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